---

name: serving-export-and-deployment description: "Guides agents serving Ludwig models, building prediction payloads, exporting artifacts, using Ray/KServe/vLLM shims, and uploading model outputs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Serving, Export, and Deployment

Use this sub-skill when the task is about ludwig serve, FastAPI endpoints, Ray Serve, KServe, vLLM serving, request payloads, export formats, MLflow export, or Hub uploads.

Start here

1. Confirm a complete trained model artifact exists. Use the inspection helper from prediction-evaluation-and-inspection if needed.
2. Check serving/export optional dependencies:

python scripts/check_serving_prereqs.py

3. Build a payload before starting a server:

python scripts/build_serving_payload.py --config config.yaml --mode predict --output payload.json

4. Read workflows.md for local server, Ray Serve, KServe, vLLM, export, and upload patterns.

Core commands

ludwig serve --model_path results/experiment_run/model --host 0.0.0.0 --port 8000
ludwig export_model --model_path results/experiment_run/model --output_path exported_model --format safetensors
ludwig export_mlflow --model_path results/experiment_run/model --output_path mlflow_model
ludwig upload hf_hub --repo_id owner/name --model_path results/experiment_run/model

Do not run servers or uploads unless the user approves long-running processes or remote side effects.